Building AI-Powered Business Model



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.14 GB | Duration: 2h 18m

Transform Your Business with AI: Strategies for Integrating AI to Drive Growth, Innovation, and Competitive Advantage


What you'll learn
Identify High-Impact AI Opportunities
Develop an AI-Powered Business Strategy
Design an AI Adoption Roadmap
Leverage Data for AI-Driven Decision Making
Implement AI Use Cases Across Key Business Functions
Address Ethical and Regulatory Challenges
Measure and Optimize AI Performance
Build and Lead Cross-Functional AI Teams
Requirements
Basic Understanding of Business and Technology Concepts
Experience in a Professional or Managerial Role
Openness to Learning New Concepts in AI and Strategy
Description
In today's fast-paced digital landscape, artificial intelligence (AI) is transforming how businesses operate, opening new avenues for innovation, efficiency, and growth. "Building AI-Powered Business Models" is designed to help you understand how to harness the potential of AI to drive value, optimize operations, and create a competitive edge in your industry. This course will guide you through the steps of designing, implementing, and scaling AI-driven strategies within your business model, with practical insights and real-world examples tailored to professionals in business, tech, and management roles.What You'll LearnIdentify Key AI Applications: Discover how AI is used across industries to enhance customer experience, streamline operations, and innovate new product lines.Build an AI-Driven Strategy: Learn how to design a business model that integrates AI effectively, creating measurable impact and aligning with your organization's goals.Implement AI Solutions for Real Results: Develop a roadmap for integrating AI projects that deliver value early and create a framework for sustainable AI growth.Manage Ethical and Practical Challenges: Understand the risks, ethics, and governance frameworks essential to building responsible and compliant AI-powered solutions.Who This Course is ForThis course is ideal for:Business leaders and managers looking to integrate AI into their strategic planningEntrepreneurs and innovators aiming to create AI-driven products or servicesTech professionals and data scientists interested in understanding the business impact of AIConsultants and analysts working with AI adoption and digital transformation initiativesWhy Take This Course?As AI becomes a cornerstone of digital transformation, understanding how to build a business model around it is crucial for staying competitive. By the end of this course, you'll have the tools and knowledge to design a sustainable AI strategy, from initial project selection to full-scale implementation. Equip yourself to lead AI-powered initiatives that create value for your organization and meet the evolving needs of the modern market.
Overview
Section 1: Introduction to AI in Business
Lecture 1 Overview of AI in Business Models
Lecture 2 Key Trends in Generative AI
Section 2: Defining Business Problems for AI
Lecture 3 Identifying Core Challenges
Lecture 4 Use Cases for AI
Lecture 5 Aligning AI with Business Goals
Section 3: Building the AI Strategy
Lecture 6 Creating an AI Adoption Roadmap
Lecture 7 Prioritizing Early Successes
Lecture 8 AI-Driven Value Creation
Section 4: Selecting AI Projects
Lecture 9 Criteria for Project Selection
Lecture 10 AI for Efficiency vs Innovation
Lecture 11 Risk Mitigation Strategies
Section 5: AI-Powered Business Models
Lecture 12 Subscription and AI-as-a-Service (AIaaS) Models
Lecture 13 Data Monetization
Lecture 14 Outcome-Based Pricing Models
Section 6: Building Cross-Functional Teams
Lecture 15 Collaboration Across Departments
Lecture 16 Involving End-Users
Lecture 17 Managing Change and Adoption
Section 7: Ethics and Risks in AI
Lecture 18 AI Governance Frameworks
Lecture 19 Bias and Fairness
Lecture 20 Regulatory Considerations
Section 8: AI in Action: Case Studies
Lecture 21 AI in Financial Services
Business Leaders and Executives,Entrepreneurs and Innovators,Managers and Project Leaders in Technology and Data Science,Consultants and Business Analysts,Anyone Interested in AI-Driven Business Strategy
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Breathwork for Healers: Science-Based Techniques



Published 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 29m | Size: 981 MB

Wellness Coaches, Reiki Practitioners, and Sound Healers


What you'll learn
Fundamentals of Experiential Breathwork and Pranayama Techniques
Facilitation of Breathing Techniques to safely administer breathwork in person and online
Leave with a Lesson plan & A Deeper Connection with your Inner knowing and Breath Practice
Receive Breathwork Certificate from Natasha Lull Health and Wellness
Requirements
No programming experience is necessary. This course will provide you with everything you need to know.
Description
Are you a coach, sound healer, or wellness practitioner looking to elevate your practice? This course is designed to help you refresh between sessions, enhance your own grounding, and create deeper, more transformative experiences for your clients.In Breathwork for Healers: Science-Based Techniques for Coaches, Reiki Practitioners, and Sound Healers, you'll dive into the science-backed foundations of breathwork and learn step-by-step how to master five powerful breathing techniques specifically chosen for emotional regulation and facilitating meditative states. With carefully curated research studies to support each technique, you'll not only deepen your personal practice but gain the confidence to guide clients into states of calm, clarity, and emotional release.What You'll Learn:The science behind breathwork and emotional regulationEvidence-based breathing techniques for managing stress and achieving mental clarityA step-by-step approach to mastering five core techniques designed to help clients achieve calm and presencePractical applications to integrate breathwork seamlessly into your sessionsWho This Course Is For:This course is ideal for coaches, sound healers, and wellness practitioners seeking to add depth to their sessions. These tools are vital for not only your clients, but for YOU to reground between sessions.Expand your toolkit with versatile techniques that create greater ease, grounding, and emotional healing for yourself and those you serve.
Who this course is for
Sound Healers, Reiki Practitioners, Wellness Coaches, Yoga instructors, Nurse practitioners, HR, parents, teachers; Anyone who is interested in breathwork for self control, nervous system regulation and to clear emotional blocks.
This course is taught by Natasha Baillleres, MA, MEd from Columbia Universities Spirit Mind Body Institute. The information you recieve is supported by ivy league evidence based research as well as founding breathing practices from Eastern Culture.
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Boosting Productivity With Time Management Using Ai Skills



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 581.53 MB | Duration: 0h 46m

Optimize your Workflow with Proven Tools and Strategies using AI Techniques at Work and Home


What you'll learn
Learn about AI tools for efficient task and schedule management
Optimize your calendar with AI-driven scheduling solutions
Track and analyze your time effectively using AI tools
Learn you to build a tangible timer to create discipline and structure
Requirements
No prior experience or specific tools required. Just bring your enthusiasm to learn and a willingness to explore AI-driven time management solutions!
This course is designed for anyone eager to improve their productivity, whether you're a busy professional, entrepreneur, or student. All you need is a computer or mobile device with internet access to follow along with the course materials and practical demonstrations.
Description
Mastering Time Management with AI ToolsAre you feeling overwhelmed by your endless to-do list? Struggling to strike a balance between work and personal life? This course is designed to help you regain control of your time with the power of AI tools.In this comprehensive course, you'll explore the essentials of effective time management and discover how AI can simplify and enhance your daily routines. We'll cover the fundamental principles of time management and address common challenges that many people face. You'll be introduced to a variety of AI tools, including ClickUp, Motion, Reclaim, Clockwise, Timely, RescueTime, and Zapier. These tools will help you streamline your tasks, manage your schedules, and improve overall efficiency.But this course goes beyond just productivity. We'll dive deep into strategies for reducing stress and achieving a more balanced life. You'll learn how to identify and eliminate energy drains, prioritize tasks that align with your goals, and make the most of each moment. By mastering these techniques, you'll not only boost your productivity but also enhance your well-being.Imagine starting your day with a clear plan, staying focused on the present moment, and feeling a sense of accomplishment as you complete your tasks. With the aid of AI tools, this vision can become a reality. You'll gain actionable strategies to minimize distractions, manage your time more effectively, and reduce stress.Whether you're looking to enhance your work efficiency or find more time for personal pursuits, this course is perfect for you. By the end of the course, you'll have a toolkit of AI-powered strategies that will help you make the most of your time, improve your productivity, and achieve a more balanced, stress-free life.Join us and embark on a journey to discover how AI can transform your approach to time management and help you lead a more organized, efficient, and fulfilling life. Your path to mastering time management starts here
Overview
Section 1: Introduction and Basics
Lecture 1 Welcome and Course Overview
Lecture 2 Understanding Time Management
Lecture 3 Introduction to AI in Time Management
Section 2: AI Tools for Task and Schedule Management
Lecture 4 AI Tools for Task and Schedule Management
Lecture 5 Diving into Best ClickUp Features, and Alternative Tools and Sites
Lecture 6 Starting to Make your Life Easier
Section 3: Reclaim your Time and Energy
Lecture 7 Exploring Trello and Others Using AI
Lecture 8 Time for Wisdom
Lecture 9 Create a Sense of Urgency and Structure, See and listen to Time!
Lecture 10 Remember This!
This course is perfect for busy professionals, entrepreneurs, and anyone looking to enhance their productivity using AI tools. Ideal for those juggling multiple responsibilities, managing teams, or seeking to optimize their personal and professional schedules. No prior experience needed-just a desire to improve time management skills with cutting-edge technology.
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Become A Data Analyst - Tableau | Python | Power Bi | Sql



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.40 GB | Duration: 10h 22m

Mastering Analytics: From Data Visualization to Business Intelligence


What you'll learn
Understand the role and responsibilities of a Data Analyst in various industries.
Gain a foundational understanding of key data analysis and visualization tools, including Tableau, Python, Google Data Studio, and Power BI.
Learn to set up Tableau Public Desktop and navigate its interface for data analysis and visualization projects.
Master the process of connecting to various data sources within Tableau and performing data joins on related datasets.
Acquire the skills to clean and preprocess data in Tableau to ensure accuracy and relevance in analysis.
Develop the ability to create compelling data visualizations and dashboards in Tableau that tell a story or reveal insights.
Install Anaconda and understand the differences between Anaconda and Miniconda for managing Python environments.
Get familiar with Jupyter Notebook as an interactive computational environment for Python programming.
Understand the basics of Python programming, including expressions, statements, data types, and variables.
Learn to work with Python data structures such as lists, tuples, dictionaries, and sets for efficient data manipulation.
Master Python's control structures, including conditional statements and loops, for complex data analysis tasks.
Explore the use of Python functions and modules to organize and reuse code effectively.
Dive into data analysis with Python using the Pandas library for data manipulation and analysis.
Practice data cleaning techniques in Python to prepare datasets for analysis.
Learn the fundamentals of data visualization in Python
Gain an introductory understanding of Google Data Studio for creating interactive reports and dashboards.
Explore the process of connecting Google Data Studio to different data sources and importing data.
Learn to create and customize various types of visualizations in Google Data Studio, including charts, tables, and geo maps.
Understand the basics of Power BI, including setting up Microsoft 365 and installing Power BI Desktop.
Master data transformation and modeling in Power BI to create compelling data visualizations.
Learn the process of publishing reports to Power BI Service and building interactive dashboards.
Acquire foundational knowledge in SQL for querying and analyzing data stored in relational databases.
Understand MySQL database concepts, installation, and the use of MySQL Workbench for database management.
Learn advanced SQL techniques for data analysis, including table joins, subqueries, and the use of aggregate functions to summarize data.
Requirements
No prior experience in data analysis or programming is required. This course starts with foundational concepts, making it accessible for beginners.
Basic Computer Literacy: Comfort with operating computers and navigating the internet will be beneficial.
Familiarity with Excel: While not mandatory, basic knowledge of Excel or any spreadsheet software can be helpful as it introduces concepts like data manipulation and simple formulas, which are foundational to data analysis.
A Computer: A laptop or desktop with internet connectivity is essential for accessing course materials, video lectures, and software used in the course.
Software Installation: You will need to install specific software such as Tableau Public (free version), Anaconda for Python, Google Data Studio (free web-based tool), and Microsoft Power BI Desktop (free version). Installation guides and resources will be provided in the course.
Web Browser: A modern web browser (like Chrome, Firefox, or Edge) will be required to access Google Data Studio and other online resources.
Description
Embark on a transformative journey to become a skilled Data Analyst with our comprehensive course: "Become a Data Analyst - Tableau | Python | Google Data Studio | BI | SQL." This meticulously designed course aims to equip you with the essential tools and techniques of data analysis, visualization, and business intelligence, ensuring you emerge as a proficient data analyst ready to tackle real-world challenges.Course Overview:Introduction to Data Analysis: Dive into the world of data analysis by understanding the pivotal role of a Data Analyst. Explore the responsibilities, tools, and the impact of data analysis in driving business decisions and strategies.Mastering Tableau for Data Visualization: Unlock the power of Tableau, the leading visualization tool, starting from setup to advanced data manipulation techniques. Learn through hands-on exercises on connecting data sources, cleaning data, and crafting compelling stories through visualizations.Python and Jupyter Notebook for Data Analysis: Venture into Python programming, a cornerstone for any Data Analyst. From basic syntax to complex functions, this section covers it all, including an in-depth exploration of Jupyter Notebook for executing Python code in an interactive environment.Exploring Google Data Studio: Navigate through Google Data Studio to create dynamic reports and dashboards. Gain proficiency in importing data, connecting to various data sources, and visualizing data to uncover insights.Analyzing Data with Power BI: Step into the world of Power BI, a premier business intelligence platform. Learn to install, connect to data, transform datasets, and visualize insights, culminating in the publication of reports and dashboards.SQL and MySQL for Data Management and Analysis: Build a strong foundation in SQL and MySQL, from database concepts to advanced data analysis techniques. Master table joins, aggregate functions, and the art of querying databases to extract meaningful information.Advanced Data Analysis Techniques: Elevate your skills with advanced SQL techniques, including inner, left, right, and self joins, subqueries, and the use of aggregate functions to perform complex data analysis.Throughout this course, you'll engage in practical exercises and projects, applying what you've learned in real-world scenarios. Whether you're new to data analysis or looking to enhance your skills, this course offers a path to mastery across the most powerful data analysis and visualization tools available today.Prepare to transform data into actionable insights and propel your career forward as a Data Analyst. Join us on this journey to mastering the art and science of data analysis.
Overview
Section 1: Introduction to Data Analysis
Lecture 1 Introduction
Lecture 2 What is a Data Analyst
Lecture 3 The role and responsibilities of a Data Analyst
Lecture 4 Overview of Data Analysis Tools
Section 2: Introduction to Tableau and Setup
Lecture 5 What is Tableau
Lecture 6 Tableau Public Desktop
Lecture 7 Tableau Public Desktop Overview: Part 1
Lecture 8 Tableau Public Desktop Overview: Part 2
Lecture 9 Tableau Online
Lecture 10 Tableau Data Sources
Lecture 11 Tableau File Types
Section 3: Data Analysis and Visualization with Tableau
Lecture 12 Connecting to a data source
Lecture 13 Join related data sources
Lecture 14 Join data sources with inconsistent fields
Lecture 15 Data Cleaning
Lecture 16 Exploring Tableau Interface
Lecture 17 Reordering Visualization
Lecture 18 Change Summary
Lecture 19 Split text into multiple columns
Lecture 20 Presenting data using stories
Section 4: Python and Jupyter Notebook Setup
Lecture 21 What is Jupyter Notebook
Lecture 22 Anaconda vs Miniconda
Lecture 23 Installing Anaconda on a Mac
Lecture 24 Verify Anaconda installation on mac
Lecture 25 Installing Anaconda on Windows
Lecture 26 Verify Anaconda on Windows
Lecture 27 What is Anaconda Navigator
Lecture 28 Introduction to Anaconda Navigator
Lecture 29 Anaconda Navigator Overview
Lecture 30 Installing Jupyter Notebook using Anaconda
Lecture 31 How to start Jupyter Notebook Server
Lecture 32 Creating a new notebook
Section 5: Python Fundamentals
Lecture 33 What is Python
Lecture 34 Python Expressions
Lecture 35 Python Statements
Lecture 36 Python Comments
Lecture 37 Python Data Types
Lecture 38 Casting Data Types
Lecture 39 Python Variables
Lecture 40 Python List
Lecture 41 Python Tuple
Lecture 42 Python Dictionaries
Lecture 43 Python Operators
Lecture 44 Python Conditional Statements
Lecture 45 Python Loops
Lecture 46 Python Functions
Section 6: Data Analysis with Python
Lecture 47 Kaggle Data Sets
Lecture 48 Tabular Data
Lecture 49 Exploring Pandas DataFrame
Lecture 50 Manipulating a Pandas DataFrame
Lecture 51 What is data cleaning
Lecture 52 Basic data cleaning
Lecture 53 What is data visualization
Lecture 54 Visualizing Qualitative Data
Lecture 55 Visualizing Quantitative Data
Section 7: Data Analysis and visualization with Google Data Studio
Lecture 56 What is Google Data Studio
Lecture 57 How to access Google Data Studio
Lecture 58 Exploring Google Data Studio Interface
Lecture 59 Data sources and connectors
Lecture 60 Importing data into data studio
Lecture 61 Connecting to sample data source
Lecture 62 Creating data visualization
Lecture 63 Importing data into Googlesheets
Lecture 64 Connecting to Googlesheets
Lecture 65 What are dimensions
Lecture 66 What are metrics
Lecture 67 Data refresh frequency
Lecture 68 Exploring edit and view modes in reports
Lecture 69 Creating a Pie Chart
Lecture 70 Creating a Bar Chart
Lecture 71 Adding a table to report
Lecture 72 Sorting data in columns
Lecture 73 Add bars to table metrics columns
Lecture 74 Creating a time series chart
Lecture 75 Customizing a time series chart
Lecture 76 Creating a Geo Chart
Lecture 77 Creating calculated fields
Lecture 78 Data cleaning using calculated fields
Lecture 79 Control Filters
Lecture 80 Adding a date range control
Lecture 81 Formatting your dashboard
Section 8: Analyzing Data and Visualization with Power BI
Lecture 82 What is Power BI
Lecture 83 Microsoft 365 Setup
Lecture 84 Exploring Microsoft 365
Lecture 85 Installing Power BI Desktop
Lecture 86 Exploring Power BI Desktop Interface
Lecture 87 Connecting to data
Lecture 88 Transforming Data
Lecture 89 Data Modelling
Lecture 90 Visualizing Data
Lecture 91 Publishing reports to Power BI Service
Lecture 92 Building a dashboard
Lecture 93 Collaborating and sharing
Section 9: Introduction to MySQL and Setup
Lecture 94 What is SQL
Lecture 95 What is MySQL
Lecture 96 Database Concepts
Lecture 97 Installing MySQL (Windows)
Lecture 98 Installing MySQL (Mac )
Lecture 99 What is MySQL Workbench
Lecture 100 Installing MySQL Workbench (Mac)
Lecture 101 MySQL Data Types
Lecture 102 Overview of using MySQL and SQL for Data Analysis
Lecture 103 Introduction to Databases
Section 10: Data Analysis with SQL
Lecture 104 Introduction to Table Joins
Lecture 105 Analysing data using SQL INNER Join
Lecture 106 Analysing data using SQL LEFT Join
Lecture 107 Analysing data using SQL RIGHT Join
Lecture 108 Analysing data using SQL SELF Join
Lecture 109 Analysing data using Sub Query
Lecture 110 Analysing data using SQL Nested Sub Query
Lecture 111 Introduction to Aggregate functions
Lecture 112 Analysing data using SQL AVG Aggregate Function
Lecture 113 Analysing data using SQL COUNT Aggregate Function
Lecture 114 Analysing data using SQL SUM Aggregate Function
Lecture 115 Analysing data using SQL MIN Aggregate Function
Lecture 116 Analysing data using SQL MAX Aggregate Function
Lecture 117 Aggregate functions in SQL GROUPBY Clause
Lecture 118 Aggregate functions in SQL HAVING Clause
Lecture 119 Filtering data with the WHERE Clause
Lecture 120 Sorting data with ORDER BY Clause
Individuals looking to pivot into a data-driven career will find this course an invaluable stepping stone. Whether you're transitioning from a non-technical role or seeking to enter the tech industry, our comprehensive curriculum will guide you through the essentials of data analysis, visualization, and business intelligence tools.,Aspiring data analysts with little to no prior experience in the field are prime candidates for this course. We start with the basics, ensuring that learners gain a solid foundation in data analysis concepts and tools, making the course ideal for those who are starting their journey in data analytics.,College students or recent graduates in fields such as business, economics, computer science, or any other discipline who wish to enhance their data analysis skills will benefit significantly. This course can complement your academic knowledge, providing practical, hands-on experience with tools and techniques used in the industry.,Working professionals in roles that involve data handling, reporting, or decision-making, such as business analysts, marketing professionals, and project managers, will find the course content directly applicable to their work. Enhancing your data analysis skills can lead to improved job performance, opportunities for advancement, or even a specialisation shift within your career.,Entrepreneurs who need to make data-driven decisions to grow their business will benefit from learning how to analyze data effectively. This course will empower you to understand your business data better, identify trends, and make informed decisions.,Individuals with a keen interest in data, technology, and analytics, looking to explore new skills or understand the world of data analysis better, will find the course engaging and enlightening. It's an excellent opportunity for personal growth and intellectual stimulation.
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AWS Machine Learning: From Basics To Hands-On Projects



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.14 GB | Duration: 2h 21m

Master AWS Machine Learning with comprehensive lessons and hands-on projects to transform data into actionable insights.


What you'll learn
Introduction to AWS Machine Learning: Understand the fundamentals of AWS Machine Learning and its key features.
Data Sourcing and Preparation: Learn the lifecycle of AML, from data ingestion to model deployment.
Managing Data Quality and Variables: Address data quality issues, including handling invalid values.
Hands-On Data Insights: Engage in practical exercises to create and manage data sources.
Building and Evaluating ML Models: Develop and fine-tune machine learning models using AWS's advanced settings.
End-to-End ML Project Management: Master the creation, management, and evaluation of ML objects in AWS.
Requirements
Basic Knowledge of AWS Services: Familiarity with core AWS services like S3, EC2, and IAM will be beneficial.
Foundational Programming Skills: Basic knowledge of Python is recommended, as it will be used for scripting and model management.
Interest in Machine Learning: No prior experience in machine learning is required, but an enthusiasm for learning how to build ML models will enhance your experience.
Description
In the era of data-driven decision-making, mastering machine learning is a valuable skill. The AWS Machine Learning Mastery: From Basics to Hands-On Projects course is designed to take you from the fundamentals of AWS Machine Learning (AML) to practical applications. Whether you are new to the field or looking to deepen your knowledge, this course offers a structured and engaging approach to mastering AWS's machine learning services. Through step-by-step guidance, real-world examples, and hands-on exercises, you will gain the skills needed to implement powerful ML models using AWS.Section-wise Writeup:Section 1: IntroductionThis section lays the foundation by introducing you to AWS Machine Learning (AML). We begin with an overview of the platform, its capabilities, and how it integrates with other AWS services. You'll learn about the key features of AWS Machine Learning and how it simplifies the process of building, training, and deploying machine learning models. By the end of this section, you'll have a clear understanding of AML's role in modern data science.Section 2: DatasourceIn this section, we dive into the critical aspect of data sourcing, which forms the backbone of any machine learning project. We start with the Lifecycle of AML, exploring the journey from data preparation to model deployment. You'll learn how to connect to various data sources, including S3 buckets, databases, and on-premises systems. Additionally, you'll discover how to create robust data schemes within AML, setting the stage for effective model training. This section ensures you are equipped to handle the complexities of data integration in AWS.Section 3: ValueThis section focuses on the value aspect of machine learning models. We address how to manage invalid values in datasets and set up variable targets for accurate predictions. You'll gain insights into the different types of ML models available in AML and how to select the best fit for your project needs. We also cover managing machine learning objects, such as datasets, models, and batch predictions, providing a comprehensive understanding of AML's functionalities.Section 4: Datasource Hands-OnLearning by doing is crucial for mastering new skills, which is why this section emphasizes practical application. You'll engage in hands-on exercises, starting with creating data sources in AML. This includes a step-by-step walkthrough on setting up and managing data sources, followed by deeper dives into extracting insights from your datasets. By the end of this section, you'll be proficient in leveraging AWS's tools to analyze and interpret data, turning raw information into actionable insights.Section 5: ML Model Hands-OnThe final section brings everything together by guiding you through the process of building, evaluating, and deploying machine learning models. You'll explore real-world examples, create ML models, and learn how to fine-tune them using advanced settings. We also cover batch predictions, enabling you to automate the process of generating predictions for large datasets. The hands-on sessions culminate in a comprehensive overview of managing ML objects in AML, ensuring you are ready to implement these techniques in practical scenarios.Conclusion:By the end of the AWS Machine Learning Mastery: From Basics to Hands-On Projects course, you will have gained a robust understanding of AWS Machine Learning. You'll be proficient in sourcing, preparing, and analyzing data, as well as building and deploying machine learning models on AWS. This course is designed to provide you with practical skills that can be directly applied in real-world scenarios, making you a valuable asset in any data-driven organization. Whether you are looking to advance your career, transition into a new role, or simply expand your knowledge, this course provides the tools and confidence needed to succeed in the dynamic field of machine learning.
Overview
Section 1: Introduction
Lecture 1 Introduction to AWS Machine Learning (AML)
Section 2: Datasource
Lecture 2 Lifecycle of AML
Lecture 3 Connecting to Data Source in AML
Lecture 4 Creating Data Scheme in AML
Section 3: Value
Lecture 5 Invaild Value and Varible Target in AML
Lecture 6 ML Models in AML
Lecture 7 Manging ML Object in AML
Section 4: Datasource Handson
Lecture 8 Creating DataSource Handson
Lecture 9 Creating DataSource Handson Continues
Lecture 10 Example of Data Insight In AML
Lecture 11 More on Data Insight In AML
Section 5: ML Model Handson
Lecture 12 ML Model Example in Data Sources
Lecture 13 Creating ML Model Evaluating
Lecture 14 Advanced Setting In ML Model
Lecture 15 Creating ML Model for Batch Prediction
Lecture 16 Batch Prediction Result
Lecture 17 Overvies of ML Model Handson
Lecture 18 ML objects Handson in ML
Aspiring Data Scientists & Machine Learning Engineers: Individuals looking to break into the field of data science and machine learning, especially those interested in leveraging AWS's powerful ML services.,Developers & Software Engineers: Developers who wish to expand their skill set by integrating machine learning capabilities into their applications.,Data Analysts & BI Professionals: Analysts aiming to enhance their data insights using predictive modeling and machine learning.,AWS Enthusiasts & Cloud Practitioners: Individuals who are already familiar with AWS services and want to explore its machine learning capabilities.,Tech Managers & Project Leads: IT managers and project leads looking to understand the potential of AWS Machine Learning for strategic decision-making.,Students & Academics: University students, researchers, and educators who want to apply AWS ML tools in academic projects or research.
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Aws Lambda: Real-World Projects And API Integrations



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.00 GB | Duration: 3h 30m

Unlock the power of serverless computing with AWS Lambda through hands-on projects, API integrations, and automation.

What you'll learn
Setting up and deploying AWS Lambda functions in Python.
Integrating Lambda functions with API Gateway for GET and POST requests.
Automating cloud tasks using AWS Lambda and DevOps principles.
Handling data transformations like XML to JSON using serverless architecture.
Deploying, testing, and managing serverless applications on AWS.
Building real-world projects to enhance your portfolio and cloud expertise.
Requirements
Basic knowledge of Python programming.
Familiarity with AWS services (AWS account setup is recommended).
Understanding of API concepts (RESTful services).
No prior experience with AWS Lambda is required, but basic cloud knowledge is beneficial.
Description
Course Introduction:Are you ready to harness the power of AWS Lambda for serverless computing? This course is designed to take you from the basics of AWS Lambda to mastering its real-world applications. You'll dive deep into building, deploying, and integrating Lambda functions with APIs, automating tasks, and even converting data formats seamlessly. Whether you're a developer, cloud engineer, or tech enthusiast, this course provides hands-on experience to boost your cloud skills and make you proficient in serverless technology. Join us and discover how to create efficient, scalable solutions using AWS Lambda!Section 1: AWS Lambda - Implementation for Arithmetic OperationsIn this section, you'll get an introduction to AWS Lambda and its core functionalities. Starting with setting up Lambda functions in Python, you'll learn how to connect these functions to an API and handle both GET and POST requests. We'll guide you through creating Lambda functions for basic arithmetic operations, testing payloads, and ensuring smooth API integration. By the end of this section, you'll have a solid understanding of how Lambda functions work and how to leverage them to handle API requests efficiently.Section 2: Project on DevOps - Automating a Task Using AWS LambdaThis section focuses on leveraging AWS Lambda for task automation, a critical skill in the DevOps toolkit. You'll embark on a project that showcases the automation of tasks using Lambda functions and API Gateway. You'll learn how to use Python scripts to interact with APIs, upload data to S3 buckets, and automate workflows using serverless architecture. We'll cover both GET and PUT methods and their implementation to build a dynamic serverless application. By the end of this section, you'll gain hands-on experience in automating cloud tasks and deploying serverless solutions.Section 3: Project on AWS Lambda - XML to JSON ConverterIn the final section, you'll tackle a comprehensive project: converting XML data to JSON using AWS Lambda. This project emphasizes practical coding, where you'll write Python scripts to transform data formats, discuss the code structure, and explore deployment strategies on AWS. You'll learn to create and test APIs for data conversion, covering everything from code execution to deployment and testing on AWS. The project concludes with a case study that showcases the end-to-end process, allowing you to apply your knowledge to real-world scenarios.Course Conclusion:By the end of this course, you will have a thorough understanding of AWS Lambda and its capabilities in automating tasks, handling API integrations, and transforming data formats. You'll have built multiple projects that showcase your new skills, making you confident in deploying serverless solutions in various professional environments. Whether you're looking to enhance your cloud capabilities, streamline operations, or build scalable serverless applications, this course equips you with the tools and knowledge to excel in the world of AWS. Join us and transform your cloud computing skills with AWS Lambda!
Overview
Section 1: AWS Lambda: Implementation for Arithmetic Operations
Lecture 1 Introduction
Lecture 2 AWS Lambda Function in Python
Lecture 3 Connecting Lambda Function to API
Lecture 4 Creating GET Request
Lecture 5 Creating POST Request
Lecture 6 Test Payload for GET
Lecture 7 Post Requests
Lecture 8 Lambda Function
Lecture 9 Lambda Function Continue
Section 2: Project on DevOps: Automating a Task Using AWS Lambda
Lecture 10 Introduction to Project
Lecture 11 Project Overview
Lecture 12 Python Function
Lecture 13 GET Python Function
Lecture 14 GET Lambda Function
Lecture 15 GET-PUT API Gateway
Lecture 16 PUT Python Function
Lecture 17 S3 Bucket Upload Overview
Lecture 18 Lambda-S3 Working and Flow
Section 3: Project on AWS Lambada: XML to JSON Converter
Lecture 19 Introduction to Project
Lecture 20 Introduction to Project Continue
Lecture 21 Coding Introduction
Lecture 22 Code Discussion
Lecture 23 Code Execution
Lecture 24 Case Study end Result
Lecture 25 AWS Deployment
Lecture 26 AWS Deployment Continue
Lecture 27 AWS API Creation
Lecture 28 AWS API Testing
Developers looking to expand their skillset in serverless computing with AWS.,Cloud Engineers who want to automate tasks and deploy scalable solutions.,DevOps Professionals interested in leveraging AWS Lambda for automation.,Data Engineers seeking to build data transformation pipelines using serverless functions.,Tech Enthusiasts and Students eager to learn about serverless architectures and AWS integration.,Anyone interested in enhancing their cloud skills for professional growth or personal projects.
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AWS Infrastructure Automation with Terraform



Last updated 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 37m | Size: 314 MB

Learn to use instances on AWS using terraform with security groups, vpc, and subnets.

What you'll learn
Learn basic terraform
Learn basic aws vpc networking
Learn to automate Ec2 instance creation
Learn to automate security groups, vpc creation
Requirements
No terraform knowledge required
Description
This course teaches you on how to use Terraform to create any number of instances on AWS. Cloud engineers or Devops can use terraform to automate instance creation. This course show the process live, also it will show how to do it with security groups, VPC and subnets. I will show you the complete process and also include the code with this course.Students should have access to an AWS account, or you cannot follow this course. Having a bit of knowledge in AWS dashboard will help, if you have no knowledge of AWS, you may find it a bit difficult to grasp the concepts.This is really short course, and will only use basic terraform commands to create and destroy instances with a virtual private network. So i dont assume you know terraform and i wont teach you advanced terraform commands. Also for any doubts and suggestion please contact me and i will be glad to answer your questions.Also please do not forget give a review for my course as i am new here and it will motivate me to create more course like this.Alright, let's enroll for this course and gain this awesome knowledge.ThanksRabbani
Who this course is for
Devops who wants to automate AWS instances with terraform
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AWS Elastic Beanstalk: AWS CI/CD with CodePipeline & Git



Published 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 29m | Size: 663 MB

Streamline your software delivery with automated CI/CD pipelines using CodePipeline, and Git!


What you'll learn
How to set up a CI/CD pipeline using AWS CodePipeline.
Configuring Git for version control in a cloud environment.
Creating and managing repositories with AWS CodeCommit.
Automating build, test, and deployment processes.
Setting up notifications and manual approvals for secure deployments.
Best practices for DevOps in AWS environments.
Requirements
Basic knowledge of AWS services and cloud computing. Familiarity with Git and version control systems. No prior experience with AWS CodePipeline or CodeCommit is required. A computer with internet access to configure the development environment.
Description
In today's fast-paced software development environment, continuous integration and continuous delivery (CI/CD) are crucial for delivering robust and efficient applications. This course is designed to equip you with the fundamental skills needed to set up a full-fledged CI/CD pipeline using AWS services. Whether you are a DevOps engineer, developer, or cloud enthusiast, this hands-on course will guide you through setting up development environments, managing configurations, and automating deployment processes. By the end of this course, you will have a solid foundation in leveraging AWS CodeCommit, CodePipeline, and Git to automate your software delivery.Section 1: IntroductionThe course begins with an introduction to the basics of DevOps and its importance in modern software development. This section sets the stage for the hands-on sessions to follow, outlining the objectives of the course and the key AWS services we will utilize. You will gain a clear understanding of how continuous integration and delivery can improve your development lifecycle, making deployments faster and more reliable.Section 2: SetupSetting up your environment correctly is essential for a smooth CI/CD pipeline. In this section, you'll learn how to configure your local and development environments to ensure compatibility with AWS services. We'll cover the essentials of setting up your environment and development tools, making sure you're ready to dive into AWS CodeCommit and CodePipeline.Lecture 2: Environment Setup - Learn the prerequisites for your development setup, including installing necessary software and configuring AWS CLI.Lecture 3: Development Environment Setup - Dive deeper into setting up your IDE and version control tools to ensure a smooth workflow.Section 3: ConfigurationThis section is the heart of the course, where you'll configure your AWS services to enable continuous integration and delivery. Starting with Git setup and progressing through AWS CodeCommit, you'll learn how to create and manage repositories. You'll also configure AWS CodePipeline to automate the build, test, and deployment process. By the end of this section, you'll have a fully functional CI/CD pipeline that can handle notifications and approvals.Lecture 4: Build and Configuration Files - Understand how to create build files and manage configurations for your projects.Lecture 5: Configuring Git - Set up Git for version control and prepare it for integration with AWS services.Lecture 6: AWS Code Commit Repository - Learn how to create and manage repositories using AWS CodeCommit for your projects.Lecture 7: CodePipeline Configuration - Step-by-step guide to configuring AWS CodePipeline for automating your CI/CD pipeline.Lecture 8 & 9: Notifications for Deployment - Explore how to set up deployment notifications to keep your team informed about the pipeline status.Lecture 10 & 11: Configuring Approval - Understand the importance of manual approvals in your pipeline and how to set them up for secure deployments.Conclusion:By the end of this course, you will have a deep understanding of setting up a complete CI/CD pipeline using AWS DevOps tools. You'll be able to automate code builds, test deployments, and streamline your software delivery process, significantly reducing time-to-market. This course is designed to help you achieve faster and more reliable software releases, making you an asset to any development team looking to enhance their DevOps practices.
Who this course is for
DevOps Engineers looking to master AWS DevOps tools for automation.
Software Developers aiming to streamline their deployment processes.
IT Professionals interested in adopting DevOps practices in their organizations.
Cloud Enthusiasts wanting to expand their AWS skill set.
Students and Fresh Graduates eager to learn CI/CD pipeline automation with AWS.
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Automate Your Workflows with Generative AI



Released: 11/2024
Duration: 20m | .MP4 1280x720, 30 fps® | AAC, 48000 Hz, 2ch | 53 MB
Level: General | Genre: eLearning | Language: English

Does your workflow need a boost? Generative AI not only automates tasks but also enhances decision-making and efficiency. In this course, workflow consultant Tamera Franklin guides you through the essentials of integrating AI into your business processes. Learn to leverage generative AI capabilities and train it for your specific needs. After this course, you'll be ready to use AI to transform your daily operations and enhance your productivity.
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Associate Professional Risk Manager Aprm Certificate



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 603.36 MB | Duration: 1h 22m

Risks, Risk Management, Definitions and Concepts, Strategic, Financial, Operational, Compliance, and Reputation Risks


What you'll learn
Risk Management
Introduction to Risk Management: Definitions and Concepts
Importance of Risk Management for Organisations
Types of Risks: Strategic, Financial, Operational, Compliance, and Reputational Risks
Risk Identification: Recognising and Assessing Risks
Risk Analysis Techniques
Risk Appetite and Risk Tolerance
Risk Mitigation Strategies: Avoidance, Reduction, Transfer, and Acceptance
Risk Monitoring and Control
Enterprise Risk Management (ERM): Integrated Approach to Managing Risks Across the Organisation
Risk Management Frameworks and Standards: COSO, ISO 31000
Requirements
For a better learning experience, we suggest you to use a laptop / mobile phone / pen and paper for taking notes, highlighting important points, and making summaries to reinforce your learning.
Description
Welcome to Program: Associate Professional Risk Manager (Associate PRM Certificate) by MTF InstituteCourse provided by MTF Institute of Management, Technology and FinanceMTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance. MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things. MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.MTF is present in 215 countries and has been chosen by more than 620 000 students.Course Authorr. Alex Amoroso is a seasoned professional with a rich background in academia and industry, specializing in research methodologies, strategy formulation, and product development. With a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where she was awarded distinction and honour for her exemplary research, Alex Amoroso brings a wealth of knowledge and expertise to the table.In addition to her doctoral studies, Ms. Amoroso has served as an invited teacher, delivering courses on to wide range of students from undergraduate level to business students of professional and executives courses. Currently, at EIMT in Zurich, Switzerland, she lectures for doctoral students, offering advanced instruction in research design and methodologies, and in MTF Institute Ms. Amoroso is leading Product Development academical domain.In synergy between academical and business experience, Ms. Amoroso achieved high results in business career, leading R&D activities, product development, strategic development, market analysis activities in wide range of companies. She implemented the best market practices in industries from Banking and Finance, to PropTech, Consulting and Research, and Innovative Startups.Alex Amoroso's extensive scientific production includes numerous published articles in reputable journals, as well as oral presentations and posters at international conferences. Her research findings have been presented at esteemed institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.With a passion for interdisciplinary collaboration and a commitment to driving positive change, Alex Amoroso is dedicated to empowering learners and professionals for usage of cutting edge methodologies for achieving of excellence in global business world.Risk management is the process of identifying, assessing, and controlling potential threats to an organization. These threats, or risks, could stem from a wide variety of sources.Here are the main focuses of risk management:Identification: Finding potential risks that could affect your organization or project. This involves brainstorming, looking at past issues, and considering what might happen in the future.Assessment: Determining the likelihood of those risks happening and how severe the impact would be if they did. This helps prioritize which risks need the most attention.Control: Taking steps to reduce the likelihood of the risks happening or to minimize their impact if they do. This might involve things like putting safety procedures in place, buying insurance, or having backup plans.Why is it important?Minimizes losses: By identifying and managing risks, companies can reduce financial losses, protect their reputation, and avoid legal issues.Improves decision-making: Risk management provides a framework for making informed decisions, considering potential downsides and opportunities.Enhances resilience: It helps companies to be prepared for unexpected events and to bounce back quickly from setbacks.Increases the likelihood of success: By addressing potential problems proactively, companies can increase their chances of achieving their goals.For managers specificallyrotects their team: Managers are responsible for the safety and well-being of their team. Risk management helps them create a safe work environment and minimize potential harm.Builds trust: Employees trust managers who are prepared and take steps to protect them from risks.Improves performance: When risks are managed effectively, employees can focus on their work without worrying about potential problems.In uncertain world, risk management is more critical than ever. It's an essential skill for companies and managers who want to thrive in the face of challenges.
Overview
Section 1: Introduction
Lecture 1 Onboarding to learning process
Section 2: Associate Professional Risk Manager APRM Certificate Course
Lecture 2 Unlocking the power of Risk Management
Lecture 3 Introduction to Risk Management Definitions and Concepts
Lecture 4 Importance of Risk Management for Organisations
Lecture 5 Types of Risks
Lecture 6 Risk Identification
Lecture 7 Risk Analysis Techniques
Lecture 8 Risk Appetite and Risk Tolerance
Lecture 9 Risk Mitigation Strategies
Lecture 10 Risk Monitoring and Control
Lecture 11 Enterprise Risk Management
Lecture 12 Risk Management Frameworks and Standards
Lecture 13 Conclusion
Section 3: Interactive part
Lecture 14 Interactive Part
Lecture 15 Congratulations with finishing from MTF
No special requirements. A course for anyone who wants to build career in business and risks
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